SpecGuard: Inference-Time Backdoor Detection For Free
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TL;DR - SpecGuard detects backdoor-triggered LLM behavior by reusing token-acceptance signals already produced during speculative decoding, adding no model-computation overhead. This enables always-on runtime monitoring without assumptions about trigger structure or extra generation passes.
- Backdoor activation creates a prediction mismatch between a clean draft model and the compromised target model, changing draft-token acceptance rates.
- The authors formalize this signal and argue that attackers cannot suppress it without weakening the backdoor.
- SpecGuard reportedly works across multiple backdoor types and model families, including stealthy attacks missed by input-level filters.
- The approach repurposes an existing inference optimization rather than adding separate detection computation.
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SpecGuard: Inference-Time Backdoor Detection For Free
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TL;DR - SpecGuard detects backdoor-triggered LLM behavior by reusing token-acceptance signals already produced during speculative decoding, adding no model-computation overhead. This enables always-on runtime monitoring without assumptions about trigger structure or extra generation passes.
- Backdoor activation creates a prediction mismatch between a clean draft model and the compromised target model, changing draft-token acceptance rates.
- The authors formalize this signal and argue that attackers cannot suppress it without weakening the backdoor.
- SpecGuard reportedly works across multiple backdoor types and model families, including stealthy attacks missed by input-level filters.
- The approach repurposes an existing inference optimization rather than adding separate detection computation.